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https://doi.org/10.2139/ssrn.4671580

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Identification of Material Parameters for Constitutive Equations
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Modeling the Response of NiTi Endodontic Files Subjected to Cyclic Non‐proportional Loading
inferredcites →https://doi.org/10.1007/978-0-387-47685-8openalex-extracted
Shape memory alloys : modeling and engineering applications
inferredcites →https://doi.org/10.1007/bf02818935openalex-extracted
Neural networks in computational mechanics
inferredcites →https://doi.org/10.1007/s11340-008-9148-yopenalex-extracted
Overview of Identification Methods of Mechanical Parameters Based on Full-field Measurements
inferredcites →https://doi.org/10.1007/s40830-018-00205-2openalex-extracted
Tensile Deformation of Superelastic NiTi Wires in Wide Temperature and Microstructure Ranges
inferredcites →https://doi.org/10.1016/j.cma.2007.03.010openalex-extracted
Generalized parameter identification for finite viscoelasticity
inferredcites →https://doi.org/10.1016/j.cma.2015.09.004openalex-extracted
Identification of material properties using indentation test and shape manifold learning approach
inferredcites →https://doi.org/10.1016/j.commatsci.2008.03.028openalex-extracted
Material parameters identification: Gradient-based, genetic and hybrid optimization algorithms
inferredcites →https://doi.org/10.1016/j.commatsci.2019.04.003openalex-extracted
A mixture of experts approach to handle ambiguities in parameter identification problems in material modeling
inferredcites →https://doi.org/10.1016/j.conbuildmat.2018.07.047openalex-extracted
Cyclic tension–compression behavior of superelastic shape memory alloy bars with buckling-restrained devices
inferredcites →https://doi.org/10.1016/j.euromechsol.2013.12.010openalex-extracted
Parameter identification of a thermodynamic model for superelastic shape memory alloys using analytical calculation of the sensitivity matrix
inferredcites →https://doi.org/10.1016/j.euromechsol.2022.104854openalex-extracted
Machine learning-assisted parameter identification for constitutive models based on concatenated loading path sequences
inferredcites →https://doi.org/10.1016/j.ijimpeng.2017.01.028openalex-extracted
A review of characterisation and parameters identification of materials constitutive and damage models: From normalised direct approach to most advanced inverse problem resolution
inferredcites →https://doi.org/10.1016/j.ijmecsci.2015.03.007openalex-extracted
Analysis of the deformation paths and thermomechanical parameter identification of a shape memory alloy using digital image correlation over heterogeneous tests
inferredcites →https://doi.org/10.1016/j.jmps.2023.105324openalex-extracted
Determination of Material Parameters in Constitutive Models Usingadaptive Neural Network Machine Learning
inferredcites →https://doi.org/10.1016/j.mechmat.2019.103085openalex-extracted
A three-dimensional phenomenological model for shape memory alloys including two-way shape memory effect and plasticity
inferredcites →https://doi.org/10.1016/j.msea.2023.145373openalex-extracted
Application of a nanoindentation-based approach for parameter identification to a crystal plasticity model for bcc metals
inferredcites →https://doi.org/10.1016/s0022-5096(98)00109-4openalex-extracted
Determination of constitutive properties fromspherical indentation data using neural networks. Part i:the case of pure kinematic hardening in plasticity laws
inferredcites →https://doi.org/10.1016/s0022-5096(98)00110-0openalex-extracted
Determination of constitutive properties fromspherical indentation data using neural networks. Part ii:plasticity with nonlinear isotropic and kinematichardening
inferredcites →https://doi.org/10.1016/s1359-6454(98)00112-8openalex-extracted
Asymmetry of stress–strain curves under tension and compression for NiTi shape memory alloys
inferredcites →https://doi.org/10.1088/0964-1726/18/10/104017openalex-extracted
Constitutive modeling and structural analysis considering simultaneous phase transformation and plastic yield in shape memory alloys
inferredcites →https://doi.org/10.1117/12.2046666openalex-extracted
Iterative calibration of a shape memory alloy constitutive model from 1D and 2D data using optimization methods
inferredcites →https://doi.org/10.2139/ssrn.4379618openalex-extracted
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